Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

Drug-induced gene expression profiling provides a lot
of useful
information covering various aspects of drug discovery and development.
Most importantly, this knowledge can be used to discover drugs’
mechanisms of action. Recently, deep learning-based drug design methods
are in the spotlight due to their ability to explore huge chemical
space and design property-optimized target-specific drug molecules.
Recent advances in accessibility of open-source drug-induced transcriptomic
data along with the ability of deep learning algorithms to understand
hidden patterns have opened opportunities for designing drug molecules
based on desired gene expression signatures. In this study, we propose
a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation),
to generate novel drug-like molecules based on desired gene expression
profiles. The model accepts desired gene expression profiles in a
cell-specific manner as input and designs drug-like molecules which
can elicit the required transcriptomic profile. The model was first
tested against individual gene-knocked-out transcriptomic profiles,
where the newly designed molecules showed high similarity with known
inhibitors of the knocked-out target genes. The model was next applied
on a triple negative breast cancer signature profile, where it could
generate novel molecules, highly similar to known anti-breast cancer
drugs. Overall, this work provides a generalized method, where the
method first learned the molecular signature of a given cell due to
a specific condition, and designs new small molecules with drug-like
properties.

药物诱导基因表达谱（Drug-induced gene expression profiling）可提供覆盖药物研发各环节的大量实用信息。尤为关键的是，此类信息可用于揭示药物的作用机制。近年来，基于深度学习的药物设计方法因能够探索庞大的化学空间、设计靶点特异性且性质优化的药物分子而备受瞩目。随着开源药物诱导转录组数据（open-source drug-induced transcriptomic data）的可获取性持续提升，加之深度学习算法能够挖掘隐藏的内在模式，为基于预期基因表达特征设计药物分子提供了全新契机。本研究提出一款名为Gex2SGen（Gene Expression 2 SMILES Generation，基因表达至SMILES生成）的深度学习模型，用于基于预期基因表达谱生成新型类药物分子。该模型以细胞特异性的预期基因表达谱作为输入，设计可诱导出目标转录组特征的类药物分子。研究首先针对单基因敲除转录组特征对该模型进行测试，结果显示新设计的分子与已知的敲除靶点基因抑制剂具有高度相似性。随后，研究将该模型应用于三阴性乳腺癌特征谱，成功生成了与已知抗乳腺癌药物高度相似的新型分子。综上，本研究提出一种通用方法：该方法首先学习特定条件下目标细胞的分子特征，进而设计出具备类药物性质的新型小分子化合物。